Delete transcribed-vietnamese-audio.py

transcribed-vietnamese-audio.py

@@ -1,167 +0,0 @@
-import os
-import datasets
-from huggingface_hub import HfFileSystem
-from typing import List, Tuple
-
-
-logger = datasets.logging.get_logger(__name__)
-fs = HfFileSystem()
-
-
-_CITATION = """
-
-"""
-_DESCRIPTION = """
-    This dataset contains transcripts from audio of Vietnamese speakers.
-"""
-_HOMEPAGE = "https://github.com/duytran1332002/vlr"
-_MAIN_REPO_PATH = "datasets/phdkhanh2507/transcribed-vietnamese-audio"
-_VISUAL_REPO_PATH = "datasets/phdkhanh2507/vietnamese-speaker-clip"
-_REPO_URL = "https://huggingface.co/{}/resolve/main"
-_URLS = {
-    "meta": f"{_REPO_URL}/metadata/".format(_MAIN_REPO_PATH) + "{channel}.parquet",
-    "visual": f"{_REPO_URL}/visual/".format(_VISUAL_REPO_PATH) + "{channel}.zip",
-    "audio": f"{_REPO_URL}/audio/".format(_MAIN_REPO_PATH) + "{channel}.zip",
-    "transcript": f"{_REPO_URL}/transcript/".format(_MAIN_REPO_PATH) + "{channel}.zip",
-}
-_CONFIGS = ["all"]
-if fs.exists(_MAIN_REPO_PATH + "/metadata"):
-    _CONFIGS.extend([
-        os.path.basename(file_name)[:-8]
-        for file_name in fs.listdir(_MAIN_REPO_PATH + "/metadata", detail=False)
-        if file_name.endswith(".parquet")
-    ])
-
-
-class TranscribedVietnameseAudioConfig(datasets.BuilderConfig):
-    """Transcribed Vietnamese Audio configuration."""
-
-    def __init__(self, name, **kwargs):
-        """
-        :param name:    Name of subset.
-        :param kwargs:  Arguments.
-        """
-        super().__init__(
-            name=name,
-            version=datasets.Version("1.0.0"),
-            description=_DESCRIPTION,
-            **kwargs,
-        )
-
-
-class TranscribedVietnameseAudio(datasets.GeneratorBasedBuilder):
-    """Transcribed Vietnamese Audio dataset."""
-
-    BUILDER_CONFIGS = [TranscribedVietnameseAudioConfig(name) for name in _CONFIGS]
-    DEFAULT_CONFIG_NAME = "all"
-
-    def _info(self) -> datasets.DatasetInfo:
-        features = datasets.Features({
-            "id": datasets.Value("string"),
-            "channel": datasets.Value("string"),
-            "duration": datasets.Value("float64"),
-            "fps": datasets.Value("int8"),
-            "audio": datasets.Value("binary"),
-            "sampling_rate": datasets.Value("int64"),
-            "transcript": datasets.Value("string"),
-        })
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(
-        self, dl_manager: datasets.DownloadManager
-    ) -> List[datasets.SplitGenerator]:
-        """
-        Get splits.
-        :param dl_manager:  Download manager.
-        :return:            Splits.
-        """
-        config_names = _CONFIGS[1:] if self.config.name == "all" else [self.config.name]
-
-        metadata_paths = dl_manager.download(
-            [_URLS["meta"].format(channel=channel) for channel in config_names]
-        )
-        audio_dirs = dl_manager.download_and_extract(
-            [_URLS["audio"].format(channel=channel) for channel in config_names]
-        )
-        transcript_dirs = dl_manager.download_and_extract(
-            [_URLS["transcript"].format(channel=channel) for channel in config_names]
-        )
-
-        audio_dict = {
-            channel: audio_dir for channel, audio_dir in zip(config_names, audio_dirs)
-        }
-        transcript_dict = {
-            channel: transcript_dir
-            for channel, transcript_dir in zip(config_names, transcript_dirs)
-        }
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "metadata_paths": metadata_paths,
-                    "audio_dict": audio_dict,
-                    "transcript_dict": transcript_dict,
-                },
-            ),
-        ]
-
-    def _generate_examples(
-        self, metadata_paths: List[str],
-        audio_dict: dict,
-        transcript_dict: dict,
-    ) -> Tuple[int, dict]:
-        """
-        Generate examples from metadata.
-        :param metadata_paths:      Paths to metadata.
-        :param audio_dict:          Paths to directory containing audios.
-        :param transcript_dict:     Paths to directory containing transcripts.
-        :yield:                     Example.
-        """
-        dataset = datasets.load_dataset(
-            "parquet",
-            data_files=metadata_paths,
-            split="train",
-        )
-        for i, sample in enumerate(dataset):
-            channel = sample["channel"]
-            audio_path = os.path.join(
-                audio_dict[channel], channel, sample["id"] + ".wav"
-            )
-            transcript_path = os.path.join(
-                transcript_dict[channel], channel, sample["id"] + ".txt"
-            )
-
-            yield i, {
-                "id": sample["id"],
-                "channel": channel,
-                "duration": sample["duration"],
-                "fps": sample["fps"],
-                "audio": self.__get_binary_data(audio_path),
-                "sampling_rate": sample["sampling_rate"],
-                "transcript": self.__get_text_data(transcript_path),
-            }
-
-    def __get_binary_data(self, path: str) -> bytes:
-        """
-        Get binary data from path.
-        :param path:    Path to file.
-        :return:        Binary data.
-        """
-        with open(path, "rb") as f:
-            return f.read()
-
-    def __get_text_data(self, path: str) -> str:
-        """
-        Get transcript from path.
-        :param path:     Path to transcript.
-        :return:         Transcript.
-        """
-        with open(path, "r", encoding="utf-8") as f:
-            return f.read().strip()